Clinical trials for Wolfram syndrome neurodegeneration: Novel design, endpoints, and analysis models.

<h4>Objective</h4>Wolfram syndrome, an ultra-rare condition, currently lacks effective treatment options. The rarity of this disease presents significant challenges in conducting clinical trials, particularly in achieving sufficient statistical power (e.g., 80%). The objective of this st...

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Main Authors: Guoqiao Wang, Zhaolong Adrian Li, Ling Chen, Heather Lugar, Tamara Hershey
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2025-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0321598
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author Guoqiao Wang
Zhaolong Adrian Li
Ling Chen
Heather Lugar
Tamara Hershey
author_facet Guoqiao Wang
Zhaolong Adrian Li
Ling Chen
Heather Lugar
Tamara Hershey
author_sort Guoqiao Wang
collection DOAJ
description <h4>Objective</h4>Wolfram syndrome, an ultra-rare condition, currently lacks effective treatment options. The rarity of this disease presents significant challenges in conducting clinical trials, particularly in achieving sufficient statistical power (e.g., 80%). The objective of this study is to propose a novel clinical trial design based on real-world data to reduce the sample size required for conducting clinical trials for Wolfram syndrome.<h4>Methods</h4>We propose a novel clinical trial design with three key features aimed at reducing sample size and improve efficiency: (i) Pooling historical/external controls from a longitudinal observational study conducted by the Washington University Wolfram Research Clinic. (ii) Utilizing run-in data to estimate model parameters. (iii) Simultaneously tracking treatment effects in two endpoints using a multivariate proportional linear mixed effects model.<h4>Results</h4>Comprehensive simulations were conducted based on real-world data obtained through the Wolfram syndrome longitudinal observational study. Our simulations demonstrate that this proposed design can substantially reduce sample size requirements. Specifically, with a bivariate endpoint and the inclusion of run-in data, a sample size of approximately 30 per group can achieve over 80% power, assuming the placebo progression rate remains consistent during both the run-in and randomized periods. In cases where the placebo progression rate varies, the sample size increases to approximately 50 per group.<h4>Conclusions</h4>For rare diseases like Wolfram syndrome, leveraging existing resources such as historical/external controls and run-in data, along with evaluating comprehensive treatment effects using bivariate/multivariate endpoints, can significantly expedite the development of new drugs.
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spelling doaj-art-4c21156aa0ec409ab89cbee0f8337c272025-08-20T02:33:20ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01205e032159810.1371/journal.pone.0321598Clinical trials for Wolfram syndrome neurodegeneration: Novel design, endpoints, and analysis models.Guoqiao WangZhaolong Adrian LiLing ChenHeather LugarTamara Hershey<h4>Objective</h4>Wolfram syndrome, an ultra-rare condition, currently lacks effective treatment options. The rarity of this disease presents significant challenges in conducting clinical trials, particularly in achieving sufficient statistical power (e.g., 80%). The objective of this study is to propose a novel clinical trial design based on real-world data to reduce the sample size required for conducting clinical trials for Wolfram syndrome.<h4>Methods</h4>We propose a novel clinical trial design with three key features aimed at reducing sample size and improve efficiency: (i) Pooling historical/external controls from a longitudinal observational study conducted by the Washington University Wolfram Research Clinic. (ii) Utilizing run-in data to estimate model parameters. (iii) Simultaneously tracking treatment effects in two endpoints using a multivariate proportional linear mixed effects model.<h4>Results</h4>Comprehensive simulations were conducted based on real-world data obtained through the Wolfram syndrome longitudinal observational study. Our simulations demonstrate that this proposed design can substantially reduce sample size requirements. Specifically, with a bivariate endpoint and the inclusion of run-in data, a sample size of approximately 30 per group can achieve over 80% power, assuming the placebo progression rate remains consistent during both the run-in and randomized periods. In cases where the placebo progression rate varies, the sample size increases to approximately 50 per group.<h4>Conclusions</h4>For rare diseases like Wolfram syndrome, leveraging existing resources such as historical/external controls and run-in data, along with evaluating comprehensive treatment effects using bivariate/multivariate endpoints, can significantly expedite the development of new drugs.https://doi.org/10.1371/journal.pone.0321598
spellingShingle Guoqiao Wang
Zhaolong Adrian Li
Ling Chen
Heather Lugar
Tamara Hershey
Clinical trials for Wolfram syndrome neurodegeneration: Novel design, endpoints, and analysis models.
PLoS ONE
title Clinical trials for Wolfram syndrome neurodegeneration: Novel design, endpoints, and analysis models.
title_full Clinical trials for Wolfram syndrome neurodegeneration: Novel design, endpoints, and analysis models.
title_fullStr Clinical trials for Wolfram syndrome neurodegeneration: Novel design, endpoints, and analysis models.
title_full_unstemmed Clinical trials for Wolfram syndrome neurodegeneration: Novel design, endpoints, and analysis models.
title_short Clinical trials for Wolfram syndrome neurodegeneration: Novel design, endpoints, and analysis models.
title_sort clinical trials for wolfram syndrome neurodegeneration novel design endpoints and analysis models
url https://doi.org/10.1371/journal.pone.0321598
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